\chapter{Conclusions and Future Work}
\label{ch:conc}

Socio-technical systems, which including human actors as an integral parts of the system, has emerged as a promising solution to increase the success rate of a software project. Since the human behaviors are different over time and the continuous evolving characteristic of the organizational structure, modern information systems, particularly socio-technical systems, need to adapt themselves according to the changes in the operation environment. Therefore, runtime self-reconfiguration becomes an important factor to the success of an STS.

Having studied about the self-reconfigurable STSs, we realize that the ability to automatically generate configurations and assess these configurations is essential to support runtime self-configuration. To this extend, this thesis work focuses on constructing a framework for generating and evaluating STS's configurations, which are the organizational model of an STS.

In this work, we have proposed an architecture of such a framework. To generate configuration, we employ AI planning technique in which an off-the-shelf planning tool, LPG-td, is used. The generated configuration is presented as a plan (or solution) containing of a list of actions, in which all system's goals are assigned to human actors and/or software components. To assess generated solution, our framework proposes two kind of evaluators to assess the configuration in both static view and dynamic view. While the static assessment analyzes the whole solution base on the list of actions inside, the dynamic assessment simulates the solution, then carries out assessment based on the simulated execution of the solution. The simulation is analyzing with respect to a set of events in order to evaluate the resilience of solutions.

We have also developed a runtime prototype based on the proposed architecture. The framework prototype are developed based on the Eclipse Modeling Framework and Eclipse Plugin Infrastructure, a popular Java framework, so that our framework can be easy integrated in an STS which is also built on these technologies.

In future, we are planning to fully support instance-level planning, which is currently limited supported, as well as instance-level simulation. Moreover, additional evaluators will be added to provide a wide range assessment e.g., risk analysis. 